Assistant Manager

Description

Roles & responsibilities

Here are some of the key responsibilities of AI Research Scientist: 

1.Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a strong publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML).

2.Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. Develop POCs and Showcase it to the stakeholders.

3.Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments.

4.Model Development and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the problem domain. Select suitable models, train them on large datasets, fine-tune hyperparameters, and optimize overall performance.

5.Algorithm Optimization: Optimize generative AI algorithms to enhance their efficiency, scalability, and computational performance through techniques such as parallelization, distributed computing, and hardware acceleration, maximizing the capabilities of modern computing architectures.

6.Data Preprocessing and Feature Engineering: Manage large datasets by performing data preprocessing and feature engineering to extract critical information for generative AI models. This includes tasks such as data cleaning, normalization, dimensionality reduction, and feature selection.

7.Model Evaluation and Validation: Evaluate the performance of generative AI models using relevant metrics and validation techniques. Conduct experiments, analyze results, and iteratively refine models to meet desired performance benchmarks.

8.Technical Mentorship: Provide technical leadership and mentorship to junior team members, guiding their development in generative AI through work reviews, skill-building, and knowledge sharing.

9.Documentation and Reporting: Document research findings, model architectures, methodologies, and experimental results thoroughly. Prepare technical reports, presentations, and whitepapers to effectively communicate insights and findings to stakeholders.

10.Continuous Learning and Innovation: Stay abreast of the latest advancements in generative AI by reading research papers, attending conferences, and engaging with relevant communities. Foster a culture of learning and innovation within the team to drive continuous improvement. 

Mandatory  technical & functional skills

·Strong programming skills in Python and frameworks like PyTorch or TensorFlow.

·In depth knowledge on Deep Learning - CNN, RNN, LSTM, Transformers LLMs ( BERT, GEPT, etc.) and NLP algorithms. Also, familiarity with frameworks like  Langgraph/CrewAI/Autogen to develop, deploy and evaluate AI agents.

·Ability to test and deploy open source LLMs from Huggingface, Meta- LLaMA 3.1, BLOOM, Mistral AI etc.

·Ability to create detailed technical architecture with scalability in mind for the AI solutions. Ability to explore hyperscalers and provide comparative analysis across different tools.

·Hands-on ML platforms  offered through GCP : Vertex AI or  Azure : AI Foundry or AWS SageMaker

Preferred technical & functional skills

—Cloud computing experience, particularly with Google/AWS/Azure Cloud Platform, is essential. With strong foundation in understating Data Analytics Services offered by Google/AWS/Azure ( BigQuery/Synapse)

—Large scale deployment of GenAI/DL/ML projects, with good understanding of MLOps /LLM Ops

Key behavioral attributes/requirements

—Ability to mentor junior developers

—Ability to own project deliverables, not just individual tasks

—Understand business objectives and functions to support data needs

#KGS

#LI-SS1

Responsibilities

Roles & responsibilities

Here are some of the key responsibilities of AI Research Scientist: 

1.Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a strong publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML). 2.Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. Develop POCs and Showcase it to the stakeholders. 3.Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments. 4.Model Development and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the problem domain. Select suitable models, train them on large datasets, fine-tune hyperparameters, and optimize overall performance. 5.Algorithm Optimization: Optimize generative AI algorithms to enhance their efficiency, scalability, and computational performance through techniques such as parallelization, distributed computing, and hardware acceleration, maximizing the capabilities of modern computing architectures. 6.Data Preprocessing and Feature Engineering: Manage large datasets by performing data preprocessing and feature engineering to extract critical information for generative AI models. This includes tasks such as data cleaning, normalization, dimensionality reduction, and feature selection. 7.Model Evaluation and Validation: Evaluate the performance of generative AI models using relevant metrics and validation techniques. Conduct experiments, analyze results, and iteratively refine models to meet desired performance benchmarks. 8.Technical Mentorship: Provide technical leadership and mentorship to junior team members, guiding their development in generative AI through work reviews, skill-building, and knowledge sharing. 9.Documentation and Reporting: Document research findings, model architectures, methodologies, and experimental results thoroughly. Prepare technical reports, presentations, and whitepapers to effectively communicate insights and findings to stakeholders. 10.Continuous Learning and Innovation: Stay abreast of the latest advancements in generative AI by reading research papers, attending conferences, and engaging with relevant communities. Foster a culture of learning and innovation within the team to drive continuous improvement. 

Mandatory  technical & functional skills

·Strong programming skills in Python and frameworks like PyTorch or TensorFlow. ·In depth knowledge on Deep Learning - CNN, RNN, LSTM, Transformers LLMs ( BERT, GEPT, etc.) and NLP algorithms. Also, familiarity with frameworks like  Langgraph/CrewAI/Autogen to develop, deploy and evaluate AI agents. ·Ability to test and deploy open source LLMs from Huggingface, Meta- LLaMA 3.1, BLOOM, Mistral AI etc. ·Ability to create detailed technical architecture with scalability in mind for the AI solutions. Ability to explore hyperscalers and provide comparative analysis across different tools. ·Hands-on ML platforms  offered through GCP : Vertex AI or  Azure : AI Foundry or AWS SageMaker

Preferred technical & functional skills

—Cloud computing experience, particularly with Google/AWS/Azure Cloud Platform, is essential. With strong foundation in understating Data Analytics Services offered by Google/AWS/Azure ( BigQuery/Synapse) —Large scale deployment of GenAI/DL/ML projects, with good understanding of MLOps /LLM Ops

Key behavioral attributes/requirements

—Ability to mentor junior developers —Ability to own project deliverables, not just individual tasks —Understand business objectives and functions to support data needs

#KGS

#LI-SS1

Qualifications

This role is for you if you have  the below

Educational qualifications

-B.Tech/Masters (MS by Research)/PhD or equivalent degree in Computer Science -Preferences to candidates from Tier 1 Colleges as IITs, NITs, IIITs, IISc, Indian Statistical Institute, etc.

Work experience

•6 to 8 Years of experience with strong record of publications (at least 4) in top tier conferences and journals

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